Enterprises deploying Internet of Things (IoT) devices are collecting large amounts of data in hopes of gaining insights to improve operations, safety, and costs. However, many organizations lack a strong data strategy for these growing IoT projects, sidelining IT and potentially putting the company at risk.

When it comes to enterprise adoption of IoT, most deployments are still in a pilot or proof-of-concept phase, according to Forrester Research senior analyst Paul Miller. These projects are often driven by operational teams, and are not necessarily linked to enterprise-wide technology strategies for cloud or data.

"A lot of these deployments are early, small, and often under the radar of central IT," Miller said. "As they become more mission critical, there will be a very real need to ensure that they do comply with things like data policies, privacy policies, and security policies. But it's still early days, and there's relatively little formal policy around IoT deployment at the moment."

Most companies are examining how to manage their existing data, in terms of how to secure and extract value from it, said Mark Hung, a research vice president at Gartner. "Both the speed and scale of data that IoT brings is a new challenge," Hung said. With so many endpoints, companies need to prepare to manage a large influx of information that must be analyzed in close to real time to gain the greatest insights, he added.

Creating an IoT data strategy

Before building an IoT data strategy, companies should ensure that they have a solid foundation for data management, security, and analytics, Hung said.

"The best way to prepare for IoT data is to first make sure that your existing data is well managed, well secured, and that you're getting value out of it," Hung said. Failing to do this means you're just collecting more data on top of the information you have you that you don't get any value from, which makes everything more complicated, he added.

The best data strategies are co-created by stakeholders including the business, the IT department, and the operations team working together, said Christian Renaud, research director of the IoT practice at 451 Research. That group of stakeholders should decide on business objectives and problems to be solved, and then talk to vendors about what is technologically possible. That way, the CFO can ask about costs and revenue, the CIO can determine where the data is stored and how it is handled, and other leaders can make sure their questions are answered too.

"The successful IoT deployments are the ones where they had broad stakeholder buy-in at the executive level," Renaud said. "It wasn't IT or the business going it alone."

In terms of creating a data strategy or policy, companies need to consider any regulations -- particularly the upcoming General Data Protection Regulation (GDPR) -- as it could impact IoT data, Miller said.

This strategy should also stipulate what data is being collected, where it is stored, and who can access it, Miller said.

For example, if a company uses a machine that offers an IoT-enabled service to analyze performance, you need to consider who has access to that data, and whether you or the manufacturer own it, Miller said. If the manufacturer does own it, then you need to determine if they can sell it to a third party.

"It's not about saying, 'You must collect this and this,'" Miller said. "It's about understanding very clearly what is being collected, where it's going, and who has rights to see it, reuse it, and monetize it."

Today, a lot of data management and analytics strategy involves storage on the enterprise platform level or cloud level, Hung said. "With IoT, just given the scale and the field data, you necessarily have to start pushing some of the costs of obtaining the storage out to the edge," he added. "I think edge computing is going to be one of the critical components to any kind of IoT management strategy."

Companies must also ensure that any IoT data strategy complies with broader data policies, including customer privacy and retention strategies, Miller said.

There is also a large difference between IoT devices used within the organization, and those that are potentially being used by customers, Miller said. In the case of the latter, there is more of a requirement for clear visibility. "The worst possible thing would be for an IoT sensor to be gathering data that no-one knows it's collecting, and sticking it in a cloud store that no-one knows is there," Miller said. "Then someone stumbles across it, and that's obviously bad news."

Finally, an IoT data strategy must consider the tools an organization may need to gain useful information from that data, Miller said. This is where analytics comes into play.

"Simply connecting a device and getting data off it isn't enough on its own," Miller added. "You need to go a step further and start using things like machine learning to actually extract some value from that."

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